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Structural Change of Gross Regional Product in the Subjects of Ural Federal District

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  • Valerij Gamukin

    (University of Tyumen)

Abstract

The important factor of the stability of the national economy is the adaptive capability of regional economies to damping of external and internal factors of risk. It occurs thanks to the variety of the developed industry structures of the economy in regions as well as to the constant process of their transformation that finds reflection in the structure of the gross regional product (GRP). It is possible to consider three main strategies of the development of the structure of regional economy: 1 the reduction of the economies of regions to the balanced condition; 2 the emphasis on the individualization of the structure of regional economy; 3 the combined strategy, when regions with various structure of economy are integrated into macro-regions in which there is a compilation of structure. In the latter case, this can result in both the leveling of the GRP structure of the territorial subjects of the Russian Federation included in the region and its convergence to macro-region indicators, in general (for example, to the federal district’s indicators). For the confirmation of this hypothesis, the analysis of GRP of the subjects included in the Ural Federal District for the period of 2005–2014 is carried out. As a result, a number of conclusions are formulated. Thus, the measurements with the use of the Ryabtsev Index and Szalai Index have shown that the GRP structure of autonomous areas is most close to the GRP structure of the federal district. At the same time, during the analyzed period, there was a reducing in a share of mining operations along with the increase in a share of GRP types referred to the auxiliary and social component of economic activity. In the federal district, there is a slow movement to a more balanced participation of regions of the district in the generation of GRP total amount. When using the author’s index of the structure determined by the double calculation of the sum of squared deviations, the tendency towards the leveling of the GRP structure of the federal district, in general, is revealed. The results of the research can be applied when carrying out different types of the analysis of dynamics and structure of socio-economic indexes.

Suggested Citation

  • Valerij Gamukin, 2017. "Structural Change of Gross Regional Product in the Subjects of Ural Federal District," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 410-421.
    • Handle: RePEc:ura:ecregj:v:1:y:2017:i:2:p:410-421
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    References listed on IDEAS

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    1. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    2. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    3. Henzel Steffen R. & Lehmann Robert & Wohlrabe Klaus, 2015. "Nowcasting Regional GDP: The Case of the Free State of Saxony," Review of Economics, De Gruyter, vol. 66(1), pages 71-98, April.
    4. Simonetta Longhi & Peter Nijkamp & Aura Reggianni & Erich Maierhofer, 2005. "Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns," International Regional Science Review, , vol. 28(3), pages 330-346, July.
    5. Yevgeniy Animitsa & Polina Animitsa & Olga Denisova, 2014. "Evolution of knowledge about distribution of productive forces," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 21-32.
    6. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    7. Kopoin, Alexandre & Moran, Kevin & Paré, Jean-Pierre, 2013. "Forecasting regional GDP with factor models: How useful are national and international data?," Economics Letters, Elsevier, vol. 121(2), pages 267-270.
    8. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    9. repec:rre:publsh:v:37:y:2007:i:1:p:64-81 is not listed on IDEAS
    10. Eric Girardin & Konstantin A. Kholodilin, 2011. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 622-643, November.
    11. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
    12. Jay Stewart, 2006. "Assessing alternative dissimilarity indexes for comparing activity profiles," electronic International Journal of Time Use Research, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)) and The International Association for Time Use Research (IATUR), vol. 3(1), pages 49-59, August.
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